The site fell over during a sale
Your team spends weeks planning a sale. The emails go out, people arrive, and within minutes the checkout page stops loading. We have seen this happen to well-run companies, not just small ones.
Everything feels fast with ten people on the site. The real question is what happens with ten thousand. We simulate that traffic, find the exact point where things slow down, and help your team fix it well before the big day.
Customers rarely wait for a slow page. They leave, and many don't come back. These are the situations that usually bring teams to us.
Your team spends weeks planning a sale. The emails go out, people arrive, and within minutes the checkout page stops loading. We have seen this happen to well-run companies, not just small ones.
No single change is to blame. A query here, an extra API call there. Without checks for performance regression, nobody notices until users start complaining.
The test environment had a tenth of the data and none of the real traffic patterns. So the results looked great and meant very little.
These terms get mixed up a lot, even by engineers. Each test is run for a different reason, and in most projects we end up using two or three of them together.
| Test type | The question it answers | Typical use |
|---|---|---|
| Load testing | Does the app stay fast at the traffic we expect? | Before every major release |
| Stress testing | Where does it break, and does it recover cleanly? | Finding the real ceiling |
| Spike testing | What happens if traffic jumps all at once? | Sales, ticket drops, TV ads |
| Endurance (soak) testing | Does it slow down after running for hours? | Catching memory leaks |
| Volume testing | Does it cope when the database gets very large? | Growing products, data migrations |
| Scalability testing | Does adding servers actually add capacity? | Capacity planning and cloud budgets |
We don't stop at a report full of graphs. As a performance engineering team, we stay with you until the slow parts are fixed and proven fixed.
JMeter is free, flexible and supports almost every protocol. Our load testing services use it, or k6 and Gatling, to recreate realistic user journeys at scale.
Our stress testing services deliberately go beyond your normal traffic. This tells you which part of the system gives way first, what users see when it does, and how quickly things return to normal afterwards.
Some problems only show up after eight or twelve hours. We keep steady load running long enough to catch slow leaks and creeping response times.
Our scalability testing services show how much each extra server really buys you, so you can plan capacity without overpaying for cloud you don't need.
Averages hide pain. We track P95 and P99 latency, the experience of your slowest users, alongside requests per second and error rates.
Slowness has to come from somewhere. We trace each request through every layer until we find the step that's holding everything else up.
During each test we keep your application performance monitoring tools open, whether that is New Relic, Datadog, Dynatrace or something else. Comparing the two side by side lets us point to the actual cause of a slowdown instead of guessing at it.
Every test ends with a report. The first page is a short summary written for managers, and the pages after it hold the charts and logs your engineers will want. We also save the results so the next test can be compared against this one.
Your mobile app, web front end and partners all hit the same APIs. We load test them directly to find limits before any client does.
Start-up time, battery drain, memory use and behaviour on a weak 4G signal, checked on real phones, plus load on the backend behind them.
Slow queries, missing indexes and lock contention are behind a lot of slow apps. We find them under load and help with database tuning until the numbers hold.
Web application load testing is only useful if it looks like real life. This is the process we follow on every project.
How many concurrent users, how many requests per second, and how fast is fast enough? We agree on numbers before writing a single script.
A test environment sized like production, filled with production-like data. Otherwise the results won't mean much.
We look at your analytics to see what visitors really do, such as how many browse, how many search and how many buy. The test scripts copy that mix, including the short pauses people take between clicks.
Load, stress, spike or soak, depending on the goal. Your monitoring stays open the whole time so we see what the servers see.
Once we know what is slowing things down, we sit with your developers to work out a fix. After the change goes in, we run the same test again to check that it made a real difference.
Layer by layer. A slow page is like a traffic jam: the queue shows up in one place, but the cause is usually somewhere further down the road.
We follow a single slow request from the browser all the way to the database, timing each hop. Whichever layer eats the most time under load is where we dig in.
Layers checked
In our experience, the database is the most common culprit, but never assume. Measure first.
All three are good. We also work with LoadRunner for enterprises that already own it. The best fit depends on your team and how you want to run the tests.
| Attribute | JMeter | k6 | Gatling | LoadRunner |
|---|---|---|---|---|
| Licence | Open source | Open source | Open source, paid enterprise edition | Commercial |
| How tests are written | GUI or XML test plans | JavaScript | Java, Kotlin or Scala code | Scripts and recorder |
| Strong point | Huge plugin ecosystem, many protocols | Lightweight and easy to run in CI | Efficient engine, detailed reports | Very wide protocol support |
| Pick it when | You need broad protocol coverage on a budget | Developers will own the tests | Your team is comfortable with JVM languages | You already have licences and legacy systems |
A big load test before launch is good. Catching a slowdown the day it's introduced is better. CI/CD performance testing makes speed something you check on every release, not once a year.
A short, light test on key endpoints. If P95 latency gets worse than the agreed threshold, the build fails.
A longer run that is compared with last week's benchmark, so gradual drift shows up early.
A full load and stress test against a production-sized environment.
Diwali, Black Friday and flash sales can bring a huge jump in visitors over a few hours. Our load testing for eCommerce peak sales starts several weeks before the event, which leaves time to fix any problems and run the test again.
Performance testing for healthcare platforms, where a slow system can delay care. We test with synthetic patient data and never touch real records.
For a SaaS company, signing one big customer can mean a sudden rise in usage. Our application performance testing tells you whether the platform can cope before the contract is signed, not after.
People are quick to delete an app that feels slow. We test the app itself on real phones and, at the same time, put load on the servers it talks to, because problems often come from both sides.
The cost depends mainly on how many user journeys we need to test, how much traffic we have to simulate, and whether you want our help fixing what we find. Most clients choose one of the three options below.
Performance audit
₹25,000 fixed
A one-off load and stress test of your key journeys, with a bottleneck report and clear recommendations.
Sale-event readiness
₹55,000 per event
Several rounds of testing before a launch or sale, help with fixes, and an engineer on call during the event itself.
Performance engineering
₹32,000 / month
Ongoing performance engineering services: tests in your pipeline, monthly benchmarks and tuning support.
Tell us about your app and any big dates coming up. A performance engineer will review your setup and, on a free 30-minute call, tell you where they'd expect trouble and how to test for it.
A performance engineer will reply within 4 hours.
to help our experts understand your business objectives and create your customized plan.